SoroushVahidi/scidocs huggingface dataset:
Sensorized Garments and Textrode-Enabled Measurement Instrumentation for Ambulatory Assessment of the Autonomic Nervous System Response in the ATREC Project
(2013)

Advances in textile materials, technology and miniaturization of electronics for measurement instrumentation has boosted the development of wearable measurement systems. In several projects sensorized garments and non-invasive instrumentation have been integrated to assess on emotional, cognitive responses as well as physical arousal and status of mental stress through the study of the autonomous nervous system. Assessing the mental state of workers under stressful conditions is critical to identify which workers are in the proper state of mind and which are not ready to undertake a mission, which might consequently risk their own life and the lives of others. The project Assessment in Real Time of the Stress in Combatants (ATREC) aims to enable real time assessment of mental stress of the Spanish Armed Forces during military activities using a wearable measurement system containing sensorized garments and textile-enabled non-invasive instrumentation. This work describes the multiparametric sensorized garments and measurement instrumentation implemented in the first phase of the project required to evaluate physiological indicators and recording candidates that can be useful for detection of mental stress. For such purpose different sensorized garments have been constructed: a textrode chest-strap system with six repositionable textrodes, a sensorized glove and an upper-arm strap. The implemented textile-enabled instrumentation contains one skin galvanometer, two temperature sensors for skin and environmental temperature and an impedance pneumographer containing a 1-channel ECG amplifier to record cardiogenic biopotentials. With such combinations of garments and non-invasive measurement devices, a multiparametric wearable measurement system has been implemented able to record the following physiological parameters: heart and respiration rate, skin galvanic response, environmental and peripheral temperature. To ensure the proper functioning of the implemented garments and devices the full series of 12 sets have been functionally tested recording cardiogenic biopotential, thoracic impedance, galvanic skin response and temperature values. The experimental results indicate that the implemented wearable measurement systems operate according to the specifications and are ready to be used for mental stress experiments, which will be executed in the coming phases of the project with dozens of healthy volunteers.
CPU and/or GPU: Revisiting the GPU Vs. CPU Myth
(2013)

Parallel computing using accelerators has gained widespre ad research attention in the past few years. In particular, using GPUs for general purpose computing has brought forth several success stories with respect to time taken, cost, power, and other metrics. Howev er, accelerator based computing has significantly relegated the role of CPUs in computation. As CPUs evo lv and also offer matching computational resources, it is important to also include CPUs in the comput ation. We call this thehybrid computing model. Indeed, most computer systems of the present age offe r a d gree of heterogeneity and therefore such a model is quite natural. We reevaluate the claim of a recent paper by Lee et al.(ISCA 20 10). We argue that the right question arising out of Lee et al. (ISCA 2010) should be how to use a CPU+ GPU platform efficiently, instead of whether one should use a CPU or a GPU exclusively. To this end, we experiment with a set of 13 diverse workloads ranging from databases, image processing, spars e matrix kernels, and graphs. We experiment with two different hybrid platforms: one consisting of a 6-c ore Intel i7-980X CPU and an NVidia Tesla T10 GPU, and another consisting of an Intel E7400 dual core CP U with an NVidia GT520 GPU. On both these platforms, we show that hybrid solutions offer good ad vantage over CPU or GPU alone solutions. On both these platforms, we also show that our solutions are 9 0% resource efficient on average. Our work therefore suggests that hybrid computing can offer tremendous advantages at not only research-scale platforms but also the more realistic scale yst ms with significant performance gains and resource efficiency to the large scale user community.
Research on data augmentation for image classification based on convolution neural networks
(2017)

The performance of deep convolution neural networks will be further enhanced with the expansion of the training data set. For the image classification tasks, it is necessary to expand the insufficient training image samples through various data augmentation methods. This paper explores the impact of various data augmentation methods on image classification tasks with deep convolution Neural network, in which Alexnet is employed as the pre-training network model and a subset of CIFAR10 and ImageNet (10 categories) are selected as the original data set. The data augmentation methods used in this paper include: GAN/WGAN, Flipping, Cropping, Shifting, PCA jittering, Color jittering, Noise, Rotation, and some combinations. Experimental results show that, under the same condition of multiple increasing, the performance evaluation on small-scale data sets is more obvious, the four individual methods (Cropping, Flipping, WGAN, Rotation) perform generally better than others, and some appropriate combination methods are slightly more effective than the individuals.
Ricci Flow for 3D Shape Analysis
(2007)

Ricci flow is a powerful curvature flow method, which is invariant to rigid motion, scaling, isometric, and conformal deformations. We present the first application of surface Ricci flow in computer vision. Previous methods based on conformal geometry, which only handle 3D shapes with simple topology, are subsumed by the Ricci flow-based method, which handles surfaces with arbitrary topology. We present a general framework for the computation of Ricci flow, which can design any Riemannian metric by user-defined curvature. The solution to Ricci flow is unique and robust to noise. We provide implementation details for Ricci flow on discrete surfaces of either Euclidean or hyperbolic background geometry. Our Ricci flow-based method can convert all 3D problems into 2D domains and offers a general framework for 3D shape analysis. We demonstrate the applicability of this intrinsic shape representation through standard shape analysis problems, such as 3D shape matching and registration, and shape indexing. Surfaces with large nonrigid anisotropic deformations can be registered using Ricci flow with constraints of feature points and curves. We show how conformal equivalence can be used to index shapes in a 3D surface shape space with the use of Teichmuller space coordinates. Experimental results are shown on 3D face data sets with large expression deformations and on dynamic heart data.
Automatic Identification of Retinal Arteries and Veins in Fundus Images using Local Binary Patterns
(2016)

Artery and vein (AV) classification of retinal images is a key to necessary tasks, such as automated measurement of arteriolar-to-venular diameter ratio (AVR). This paper comprehensively reviews the state-of-the art in AV classification methods. To improve on previous methods, a new Local Binary Pattern-based method (LBP) is proposed. Beside its simplicity, LBP is robust against low contrast and low quality fundus images; and it helps the process by including additional AV texture and shape information. Experimental results compare the performance of the new method with the state-of-the art; and also methods with different feature extraction and classification schemas.
Improving code review effectiveness through reviewer recommendations
(2014)

Effectively performing code review increases the quality of software and reduces occurrence of defects. However, this requires reviewers with experiences and deep understandings of system code. Manual selection of such reviewers can be a costly and time-consuming task. To reduce this cost, we propose a reviewer recommendation algorithm determining file path similarity called FPS algorithm. Using three OSS projects as case studies, FPS algorithm was accurate up to 77.97%, which significantly outperformed the previous approach.
Side-channel leakage aware instruction scheduling
(2017)

Speed-optimized side-channel protected software implementations of block ciphers are important for the security of embedded IoT devices based on general-purpose microcontrollers. The recent work of Schwabe et al. published at SAC 2016 introduced a bit-sliced implementation of AES and a first-order Boolean-masked version of it, targeting ARM Cortex-M CPU cores. The authors claim to be secure against timing as well as first-order power and electromagnetic side-channel attacks. However, the author's security claims are not taking the actual leakage characteristics of the underlying CPU architecture into account, hence making the scheme potentially vulnerable to first-order attacks in practice. In this work we show indeed that such a masking scheme can be attacked very easily by first-order electromagnetic side-channel attacks. In order to fix the issue and provide practical first-order security, we provide a strategy to schedule program instructions in way that the specific leakage of the CPU does not impair the side-channel countermeasure.
Rationalizing Sentiment Analysis in Tensorflow
(2017)

Sentiment analysis using deep learning models is a leading subject of interest in Natural Language Processing that is as powerful as it is opaque. Current state-ofthe-art models can produce accurate predictions, but they provide little insight as to why the model predicted this sentiment. Businesses relying on these models might be less likely to act on insight given the lack of evidence for predictions. These people would be more likely to trust such predictions if a brief explanation of the outcome is provided. Recent work by Lei et al [4]. has set forth a framework for a multi-aspect sentiment analysis concurrently providing text rationalization with each prediction. This framework sets forth a two-part approach, which summarizes a review and predicts a sentiment. In this paper, we explore the performance of this framework, seeking to recreate and improve upon it in TensorFlow.
Elimination of the musical noise phenomenon with the Ephraim and Malah noise suppressor
(1994)

This paper presents a study of the noise suppression technique proposed by Ephraim and Malah. This technique has been used recently for the restoration of degraded audio recordings because it is free of the frequently encountered ‘musical noise’ artifact. It is demonstrated how this artifact is actually eliminated without bringing distortion to the recorded signal even if the noise is only poorly stationary.
Enhancement and bandwidth compression of noisy speech
(1979)

Over the past several years there has been considerable attention focused on the problem of enhancement and bandwidth compression of speech degraded by additive background noise. This interest is motivated by several factors including a broad set of important applications, the apparent lack of robustness in current speech-compression systems and the development of several potentially promising and practical solutions. One objective of this paper is to provide an overview of the variety of techniques that have been proposed for enhancement and bandwidth compression of speech degraded by additive background noise. A second objective is to suggest a unifying framework in terms of which the relationships between these systems is more visible and which hopefully provides a structure which will suggest fruitful directions for further research.
Narrative Schema Stability in News Text
(2018)

We investigate the stability of narrative schemas (Chambers & Jurafsky, 2009) automatically induced from a news corpus, representing recurring narratives in a corpus. If such techniques produce meaningful results, we should expect that small changes to the corpus will result in only small changes to the induced schemas. We describe experiments involving successive ablation of a corpus and cross-validation at each stage of ablation, on schemas generated by three different techniques over a general news corpus and topically-specific subcorpora. We also develop a method for evaluating the similarity between sets of narrative schemas, and thus the stability of the schema induction algorithms. This stability analysis affirms the heterogeneous/homogeneous document category hypothesis first presented in Simonson & Davis (2016), whose technique is problematically limited. Additionally, increased ablation leads to increasing stability, so the smaller the remaining corpus, the more stable schema generation appears to be. We surmise that as a corpus grows larger, novel and more varied narratives continue to appear and stability declines, though at some point this decline levels off as new additions to the corpus consist essentially of “more of the same.”
Content Centric Networking in tactical and emergency MANETs
(2010)

Reliable and secure content distribution in a disruptive environment is a critical challenge due to high mobile and lossy channels. Traditional IP networking and wireless protocols tend to perform poorly. In this paper, we propose Content Centric Networking (CCN) for emergency wireless ad hoc environments. CCN is a novel communication architecture capable to access and retrieve content by name. This new approach achieves scalability, security, and efficient network resource management in large scale disaster recovery and battlefield networks. Existing Internet CCN schemes cannot be directly applied to wireless mobile ad hoc networks due to different environments and specific limitations. Thus, we must extend the CCN architecture by introducing features and requirements especially designed for disruptive networks. We prove feasibility and performance gain of the new design via implementation and experimentation.
Tunable VHF Miniaturized Helical Filters
(2014)

In this paper, a miniaturization technique and a tuning concept that enable the realization of highly miniaturized and frequency reconfigurable helical filters are reported for the first time. The proposed filter design is based on tunable helical resonators with an internal volume of only 8.6 cm 3 and variable capacitive loading. A second-order bandpass filter was built and measured as a proof-of-concept demonstrator. The helical filter is manufactured with a CNC process and tuning is achieved through piezoelectrically actuated tuners, which are bonded over the helix apex. A measured center frequency tuning between 257-365 MHz was realized with an insertion loss between 0.3-3.2 dB including connector losses. The measured filter bandwidth varied between 6%-7.7%. Tunable resonators were also manufactured so as to extract the unloaded quality factor. It was measured between 392-639 for a center frequency tuning of 2.5:1 (156.5-395 MHz).
Blind Speech Separation and Enhancement With GCC-NMF
(2017)

We present a blind source separation algorithm named GCC-NMF that combines unsupervised dictionary learning via non-negative matrix factorization NMF with spatial localization via the generalized cross correlation GCC method. Dictionary learning is performed on the mixture signal, with separation subsequently achieved by grouping dictionary atoms, at each point in time, according to their spatial origins. The resulting source separation algorithm is simple yet flexible, requiring no prior knowledge or information. Separation quality is evaluated for three tasks using stereo recordings from the publicly available SiSEC signal separation evaluation campaign: 3 and 4 concurrent speakers in reverberant environments, speech mixed with real-world background noise, and noisy recordings of a moving speaker. Performance is quantified using perceptually motivated and SNR-based measures with the PEASS and BSS Eval toolkits, respectively. We evaluate the effects of model parameters on separation quality, and compare our approach with other unsupervised and semi-supervised speech separation and enhancement approaches. We show that GCC-NMF is a flexible source separation algorithm, outperforming task-specific approaches in each of the three settings, including both blind as well as several informed approaches that require prior knowledge or information.
Anomaly detection approach using hybrid algorithm of data mining technique
(2017)

The excessive use of the communication networks, rising of Internet of Things leads to increases the vulnerability to the important and secret information. advance attacking techniques and number of attackers are increasing radically. Intrusion is one of the main threats to the internet. Hence security issues had been big problem, so that various techniques and approaches have been presented to address the limitations of intrusion detection system such as low accuracy, high false alarm rate, and time consuming. This paper proposes a hybrid machine learning technique for network intrusion detection based on combination of K-means clustering and Sequential Minimal Optimization (SMO) classification. It introduces hybrid approach that able to reduce the rate of false positive alarm, false negative alarm rate, to improve the detection rate and detect zero-day attackers. The NSL-KDD dataset has been used in the proposed technique.. The classification has been performed by using Sequential Minimal Optimization. After training and testing the proposed hybrid machine learning technique, the results have shown that the proposed technique (K-mean + SMO) has achieved a positive detection rate of (94.48%) and reduce the false alarm rate to (1.2%) and achieved accuracy of (97.3695%).
A Deep Network with Visual Text Composition Behavior
(2017)

While natural languages are compositional, how state-of-the-art neural models achieve compositionality is still unclear. We propose a deep network, which not only achieves competitive accuracy for text classification, but also exhibits compositional behavior. That is, while creating hierarchical representations of a piece of text, such as a sentence, the lower layers of the network distribute their layer-specific attention weights to individual words. In contrast, the higher layers compose meaningful phrases and clauses, whose lengths increase as the networks get deeper until fully composing the sentence.
Balanced-to-Unbalanced Bandpass Filters and the Antenna Application
(2008)

Novel balanced-to-unbalanced (balun) bandpass filters are presented and carefully examined. By properly converting a symmetric four-port balanced-to-balanced bandpass filter to a three-port device, with two ports forming a balanced output and one port remaining unbalanced, a novel three-port balun bandpass filter with both functions of balun and bandpass filter may be realized. Specifically, a balun bandpass filter is implemented with its center frequency at 0.99 GHz and a 3-dB fractional bandwidth (Bomega) of 10.1%. The implemented balun bandpass filter presents an excellent in-band balance performance with common-mode rejection ratio (Rcm) better than 41 dB (the corresponding amplitude and phase imbalances are within 0.003deg and 1deg) over the pass- band. In this study, a wideband balun bandpass filter is also implemented so as to improve the wideband performance of the quasi- Yagi antenna. Being integrated with the proposed wideband balun bandpass filter, the implemented quasi-Yagi antenna achieves a bandwidth of 48% for IS11 | < -10 dB, with front-to-back ratio better than 13 dB, cross-polarization smaller than -20 dB, and peak gain of 3-5 dBi within the wide operating bandwidth.
Active defence through deceptive IPS
(2017)

Modern security mechanisms such as Unified Threat Management (UTM), Next-Generation Firewalls and Security Information and Event Management (SIEM) have become more sophisticated over recent years, promising advanced security features and immediate mitigation of the most advanced threats. While this appears promising, in practice even this cutting-edge technology often fails to protect modern organisations as they are being targeted by attacks that were previously unknown to the security industry. Most security mechanisms are based on a database of previously known attack artefacts (signatures) and they will fail on slightly modified or new attacks. The need for threat intelligence is in complete contrast with the way current security solutions are responding to the threats they identify, as they immediately block them without attempting to acquire any further information. In this report, we present and evaluate a security mechanism that operates as an intrusion prevention system which uses honeypots to deceive an attacker, prevent a security breach and which allows the potential acquisition of intelligence on each intrusion attempt. a aThis article is published online by Computer Weekly as part of the 2017 Royal Holloway information security thesis series http://www.computerweekly.com/ehandbook/Active-defence-through-deceptive-IPS. It is based on an MSc dissertation written as part of the MSc in Information Security at the ISG, Royal Holloway, University of London. The full thesis is published on the ISG’s website at https://www.royalholloway.ac.uk/isg/.
Gait Generation With Smooth Transition Using CPG-Based Locomotion Control for Hexapod Walking Robot
(2016)

This paper presents a locomotion control method based on central pattern generator (CPG) for hexapod walking robot to achieve gait generation with smooth transition. By deriving an analytical limit cycle approximation of the Van der Pol oscillator, a simple diffusive coupling scheme is proposed to construct a ring-shape CPG network with phase-locked behavior. The stability of the proposed network is proved using synchronization analysis with guaranteed uniform ultimate boundedness of the synchronous errors. In contrast to conventional numerical methods in tuning parameters of the CPG network, our method provides an explicit result that could analytically determine the network parameters to yield the rhythmic waveforms with prescribed frequency, amplitude, and phase relationship among neurons. Employing the proposed network to govern the swing/stance phase according to the profile of the resulting CPG signals, a locomotion control strategy for the hexapod robot is further developed to manipulate the leg movements during the gait cycle. By simply adjusting the phase lags of the CPG network, the proposed control strategy is capable of generating stable walking gaits for hexapod robots and achieving smooth transition among the generated gaits. The simulation and experimental results have demonstrated the effectiveness of the proposed locomotion control method.
Microbial Fuel Cells for Wastewater Treatment
(2012)

1.1 Screening The influent is strained to remove all large objects carried in the sewage stream. This is most commonly performed with an automated mechanically-raked bar screen in modern plants serving large populations, whilst in smaller or less modern plants a manually-cleaned screen may be used. The raking action of a mechanical bar screen is typically paced according to the accumulation on the bar screens and/or flow rate. The solids are collected and later disposed of in landfill or incinerated. Bar screens or mesh screens of varying sizes may be used to optimise solids removal, so as to trap and remove the floating matter, such as pieces of cloth, paper, wood, kitchen refuse, etc. These floating materials will choke pipes or adversely affect the working of the pumps if not removed. They should be placed before the grit chambers. However, if the quality of grit is not of much importance, as in the case of landfilling etc., screens may even be placed after the grit chambers. They may sometimes be accommodated in the body of the grit chambers themselves.